With an expectation that machine-readable tables of structured experimental data add research value.
The ASCII smaller text set null character is an escape zero “\0” or 0x00 digital hash.
The full Unicode character set is consistent as linked to binary and contains a null character where null is U+0000 with symbol U+2400 (and is used as a string terminator in the C programming language).
Given digital information can be of different types (such as string or numeric among others), and that the value of zero is not null, still previous and active sentinel null character representations include the “-9999” and “NA”. Yet a control character requires a unique binary hash designation to be consistently stable. I have recently seen regular non-standard data error values returned including, “NAN”, “ERR”, and “INF”, among others (Excel has a #NAME? construct, and there is the SQL regular NULL). Software de facto handles data type and memory allocation because each data element type is comprehended into different memory configurations and sizes at run time.
In Python, the generic data object type is None or a NoneType, where the included 1985 standard numerical IEEE 754 floating-point representation is NaN.
In NumPy, the typical np.nan provides for the None, including the positive and negative infinity, and the not a number designations.
NumPy is the basis for the Pandas DataFrame None representation, which automatically converts across the object and number (until a native Pandas Null emerges).
This is a tricky question but I love how detailed the question is.
The fact that you want a generic character to represent a null state for different data types is an indication your data is in its raw state. If this is the situation I don,t see any reason you would want to replace the null values for different data types with one common character.
If the above is not the case, I would recommend using different characters for different data types (eg. empty string for strings, and NaN for numbers). You should also consider other strategies for handling null values that suit your use case ( eg. dropping the data, using average, using previous values).
Above all, you should add some metadata to describe how the null values were handled so that whoever works with your dataset would understand the changes made in the dataset.
Thank you Boadu Asare
Can there be value in converting a raw dataset to a “pre-processed” dataset; one that contains quality verified raw values, where instances of known error such as the generated null types, missing elements, or values out of parameter due to originator documented engineering error like fundamental communication or transcription hardware failures have been handled to support a generic, future, or unspecified analysis?
Perhaps the point is moot if we assume that each analysis occurrence is required to independently assess the quality of all data encountered, and that analysis ready status data support cannot be staged by data generators into selected datasets to provide a specific data clarification and control function.
However, to contextualize the numeric frame, and exterior to handling of the data values, a description of the data and experimental parameters can be helpful, as well is a robust commentary illustrating influential and ancillary happenstance. The creation and inclusion of additional textual and photographic exhibit can support subsequent data management and analysis and could therefore be recommended as a practice.
For example, one attempt to provide additional data description supporting raw datasets is available in the ten volume set of reflectance and other cotton phenotyping measurements presented as, “The Bronson Files”, in the Agricultural Data Commons of the National Agricultural Library - https://data.nal.usda.gov/dataset/bronson-files-dataset-10-field-113-2018-cotton.
Note to include the R language - is.na, is.nan, is.null, and is.finite functionality; as well as the relevant null handling approaches intrinsic to MATLAB, Java & JavaScript, Julia, Scala, SAS, and C & C++.
When you group data in SQL, when you see the number “-9999”, you understand that this is an error in the data or they were not recorded. I immediately realized this when I worked with data on cotton and guayule(Matthew and Bronson).
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